DYNAMIC RE-LOAD BALANCING ALGORITHM FOR EFFICIENT DATA PARTITIONING IN BIG DATA APPLICATIONS

Abstract

Big data applications have become a cornerstone of modern data processing, revolutionizing industries through the analysis of vast datasets. However, the efficient management of these datasets presents significant challenges, particularly in distributed environments where data partitioning is a fundamental operation. This paper presents a dynamic re-load balancing algorithms designed to continuously adapt and optimize data partitioning in the evolving landscape of big data applications. Effective data partitioning is paramount in large-scale distributed systems for achieving parallelism, reducing query response times, and ensuring equitable resource utilization. Inherent data skew, fluctuations in query patterns, and the addition or removal of resources can lead to imbalanced workloads and performance bottlenecks. To address these issues, we introduce dynamic re-load balancing strategies that dynamically re-evaluate and adjust partitioning decisions based on real-time feedback and evolving system conditions. Through extensive performance evaluations, we demonstrate the considerable advantages of dynamic re-load balancing in big data applications. Our findings reveal significant improvements in system efficiency, reduced query response times, and enhanced scalability. Furthermore, we assess the robustness of our algorithms under diverse conditions, highlighting their ability to maintain optimal performance as workloads and resource availability evolves.